Elsevier

Neurocomputing

Volume 140, 22 September 2014, Pages 283-290
Neurocomputing

Fuzzy controller for dynamic performance improvement of a half-bridge isolated DC–DC converter

https://doi.org/10.1016/j.neucom.2014.03.010Get rights and content

Abstract

Due to the switching property included in their structure, half-bridge power electronic DC–DC converters have a nonlinear behavior and their controller design is accompanied with complexities. The use of small signal model of the converter in conjunction with artificial intelligence in a robust controller structure guarantees excellent dynamic response. This work implements the fuzzy controller for converter circuit to improve the dynamic performance of asymmetric half-bridge DC–DC converter. It establishes fuzzy controller to overcome the limitations of the conventional PI controller. The conventional controller has lower loop band width and high distortion coefficient. So the system response is sluggish. In this work fuzzy logic is used to improve the characteristics of the DC–DC converter system by controlling its duty cycle. The fuzzy controller output is compared with a PI controller for five different operating conditions. The results show that the fuzzy controller is able to achieve faster transient response, less peak overshoot, better rejection of disturbances and is less dependent on the operating point of the converter.

Introduction

It is always desirable for power converters that the output voltage remains unchanged in both steady state and transient state operations whenever the supply voltage or load current is disturbed. It means that the output voltage is independent of the supply voltage and the load current. Many control methods are used for the control of switched mode DC–DC converters and the simple low cost controller structure is always in demand for most industrial and high performance applications. Every control method has some advantages and drawbacks due to which the particular control method is considered as a suitable control method under some specific conditions, compared to other control methods.

The input to power converters is often an unregulated rectified line voltage, which will fluctuate due to changes in the line voltage magnitude. When the converter is employed in an open loop mode, it exhibits poor voltage regulation and unsatisfactory dynamic behavior and hence this converter is generally equipped with closed loop control for output voltage regulation. The objective of a control system design is to make a system behave in a useful fashion, causing its output to track a desired reference input even in the presence of noise and disturbances.

The choice of a control method plays a very critical role in the performance of converters. Traditional frequency domain methods are predominantly used in controller design [1], which is based on a linear small signal model of the converter concerned. The application of a fuzzy controller for mobile welding industries to guarantee a constant current during welding to improve welding quality is presented in [2]. In this paper fuzzy controller is proposed to perform the operation under unfavorable condition and produce the output according to that particular desired level. Application of fuzzy controller for the basic DC–DC converter is presented in [3]. High performance controller can be designed from more accurate nonlinear model but it leads to very complicated control algorithms that are not suitable for practical implementation [3]. The other method of design is to employ heuristic reasoning based on human experience [4]. A neural network based controller performs the specific condition for which it is designed, so it is not suitable for high frequency converters [5]. Another method of controller design is based on system transfer function [6], [7], [8]. A genetic based hybrid fuzzy proportional integral derivative controller for industrial motor drives is presented in [9], [10]. In the control system, one of the main components is the controller, which generates the appropriate control signal for the physical system to regulate the system performance. Asymmetric half-bridge DC–DC converters are switched power electronic circuits and the modes of operation of the converters vary from ON to OFF states. Hence these power converter systems are variable structure systems; the design of the feedback controller for these converters through linear control theory is not viable. The aim of feedback control is to convert the unregulated DC input into a controlled DC output at a desired level and to keep the DC output at this level, if there are any variations of the load.

The half-bridge DC–DC converter uses a Pulse Width Modulation (PWM) technique, whereby duty cycle of the pulse is used to activate the switches to pass power from the source to load. So the average power transferred within the converter circuit is related to the duty cycle. Conventional PI controllers are used to generate pulses to control the operation of power electronic converters but it will not give an accurate performance when there is a change in system parameters or when it is subjected to large variation in the load. Also, conventional controllers often fail to produce a model that has the actual characteristics of the system. Artificial intelligence based techniques such as fuzzy logic control can overcome these difficulties.

Fuzzy logic provides an effective means of capturing the approximate, inexact nature of the real world. The fuzzy control is a practical alternative for a variety of challenging control applications, since it is a convenient method for constructing nonlinear controllers by the use of heuristic information. In this work, the fuzzy controller is used to improve the dynamic performance of the asymmetric half-bridge DC–DC converter. The results of fuzzy controller are compared with the conventional PI controller to show its importance.

Section snippets

Half-bridge converter circuit

The half-bridge DC–DC converter topology is conceptually shown in Fig. 1. The asymmetric half-bridge converter is one of the complementary driven PWM converter topology which presents an inherent zero voltage switching capability. The converter has the advantages of low voltage/current stress level, low conduction loss and constant frequency control.

Switches S1 and S2 in converter are driven asymmetrically. Dead time is introduced to prevent the cross conduction between S1 and S2 as well as to

Steady state analysis and small signal modeling

Small signal model is the basis for optimized controller design. Especially for such a complicated converter system, an effective model will be helpful to realize closed loop control and furthermore to optimize the converter dynamics. The symbolic derivation of these converter transfer function is fairly tedious and it is difficult to obtain values of poles and zeros for analysis. Alternatively, the dynamics of the converter can be described in a matrix form. The small signal model provides the

PI controller design

The proportional integral controller is one of the most common types of feedback controller that is used in dynamic systems due to its simplicity, applicability and ease of implementations. It is used in the wide variety of control systems due to its simple structure and robust performance. This controller is widely used in many different areas such as aerospace, process control, manufacturing, robotics and automation system. The efforts being made to its design justify its popularity. If a

Design of the fuzzy controller

Controllers for DC–DC converters are usually designed based on the mathematical models. To obtain a certain performance objective, an accurate model is essential. To achieve a stable and fast response, two solutions are possible. One is to develop a more accurate model for the converter. However the model may become too complex to use in controller development. A second solution is to use a nonlinear controller. Since fuzzy controllers do not require a precise mathematical model, it is well

Experimental results

In this paper, the half-bridge DC–DC converter performance is obtained from the PI controller and fuzzy controller for five different operating conditions as follows:

  • Condition 1—Actual input voltage with maximum load condition: Vin=36 V and Io=20 A.

  • Condition 2—Actual input voltage with minimum load condition: Vin=36 V and Io=5 A.

  • Condition 3—Minimum input voltage with normal load condition: Vin=30 V and Io=10 A.

  • Condition 4—Maximum input voltage with maximum load condition: Vin=40 V and Io=20 A.

  • Condition

Conclusion

Being highly complex and nonlinear, the half-bridge DC–DC converter is very difficult to control with conventional methods and linearization techniques. To achieve the optimum dynamic response, fuzzy controller is designed in this work to regulate the duty cycle of the converter, thereby controlling the output voltage. Control surfaces are designed for the fuzzy controller.

The fuzzy controlled half-bridge converter is tested under different operating conditions and it is shown that the fuzzy

Dr. A. Gnana Saravanan received his B.E degree in Electrical and Electronics Engineering, M.E. degree in Power Electronics and Drives and Ph.D degree in Electrical Engineering from Anna University, Chennai in 2005, 2009 and 2013 respectively. He has published three research papers in International Journals, two papers in International Conferences and five papers in National Conferences. He is interested in the research areas of power converter design, soft switching and artificial intelligent

References (10)

  • Rakesh K. Misra et al.

    Efficient ANN method for post-contingency status evaluation

    Int. J. Electr. Power Energy Syst.

    (2010)
  • Cetin Elmas et al.

    Adaptive fuzzy logic controller for DC–DC converters

    Expert Syst. Appl.

    (2009)
  • Zahra Malekjamshidi et al.

    Operation of a fuzzy controlled half bridge DC converter as a welding current source

    Telkomnika

    (2012)
  • J. Liang et al.

    State estimation for coupled uncertain stochastic networks with missing measurements and time varying delays: the discrete time case

    IEEE Trans. Neural Netw.

    (2009)
  • H. Huang et al.

    Robust state estimation for uncertain neural networks with time-varying delay

    IEEE Trans. Neural Netw.

    (2008)
There are more references available in the full text version of this article.

Cited by (11)

  • A new control method based on type-2 fuzzy neural PI controller to improve dynamic performance of a half-bridge DC–DC converter

    2016, Neurocomputing
    Citation Excerpt :

    The using of a fuzzy controller is the newest presented method for dynamic performance improvement of a DC–DC converter. Details of this method are described in Ref. [2]. The simulation results for five different operating points are shown in Figs. 11–15.

  • DC/DC Modular Multilevel Converters for HVDC Interconnection: A Comprehensive Review

    2022, International Transactions on Electrical Energy Systems
View all citing articles on Scopus

Dr. A. Gnana Saravanan received his B.E degree in Electrical and Electronics Engineering, M.E. degree in Power Electronics and Drives and Ph.D degree in Electrical Engineering from Anna University, Chennai in 2005, 2009 and 2013 respectively. He has published three research papers in International Journals, two papers in International Conferences and five papers in National Conferences. He is interested in the research areas of power converter design, soft switching and artificial intelligent techniques.

Dr. M. Rajaram received his B.E. degree in Electrical and Electronics Engineering in 1981 and M.E. degree in Power Systems in 1988. Besides having a strong technical expertise and analytical skills, he received his Ph.D degree in 1994. He has contributed to the areas of Computer Networks, High Voltage Engineering, Measurement and Instrumentation, Adaptive Controller, Electro Magnetic Theory and Intelligent Computing. He has 157 publications in renowned research journals, 111 research publications in International Conferences, 73 research publications in National Conferences, more than 100 technical reports and six technical books some of which he has co-authored. Currently, he is the Vice-Chancellor of the Anna University, Chennai.

1

Tel.: 09942778202.

View full text